Adaptive early exit techniques in image correlation

ABSTRACT

Images are obtained for image compression. The images are compared using sum of absolute difference devices, which have arithmetic parts, and accumulators. The sign bits of the accumulators are determined at a time of minimum distortion between two images. These sign bits are associated with sets of probabilistically-similar parts. When other sets from that set are obtained later, an early exit is established.

BACKGROUND

Image compression techniques can reduce the amount of data to betransmitted in video applications. This is often done by determiningparts of the image that have stayed the same. The “motion estimation”technique is used in various video coding methods.

Motion estimation is an attempt to find the best match between a sourceblock belonging to some frame N and a search area. The search area canbe in the same frame N, or can be in a search area in a temporallydisplaced frame N-k.

These techniques may be computationally intensive.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects will now be described in detail with referenceto the accompanying drawings, wherein:

FIG. 1 shows a source block and search block being compared against oneanother;

FIG. 2 shows a basic accumulation unit for measuring distortion;

FIGS. 3 a and 3 b shows different partitioning of the calculations amongmultiple SAD units;

FIG. 4 shows a tradeoff between early exit strategy calculations and anactual total calculation;

FIG. 5 shows a flowchart of calculating distortion in a specific deviceand early exit strategy;

FIG. 6 a shows an early exit using an early exit flag;

FIG. 6 b shows early exit using a hardware status register;

FIG. 7 shows a flowchart of operation of the adaptive early exitstrategy.

DETAILED DESCRIPTION

Motion estimation is often carried out by calculating a sum of absolutedifferences or “SAD”. Motion estimation can be used in many differentapplications, including, but not limited to cellular telephones that usevideo, video cameras, video accelerators, and other such devices. Thesedevices can produce video signals as outputs. The SAD is a calculationoften used to identify the lowest distortion between a source block anda number of blocks in a search region search block. Hence the best matchbetween these blocks. One way of expressing this is${{SAD} = {\sum\limits_{i = 0}^{N - 1}\quad{\sum\limits_{j = 0}^{n - 1}\quad{{{a\left( {i,j} \right)} - {b\left( {i,j} \right)}}}}}},{N = 2},4,8,16,32,64.$

Conceptually what this means is that a first frame or source block (N)is divided into component parts of M×N source blocks 100. These arecompared to a second frame (N-K) 102. The frames can be temporallydisplaced, in which case k≠0. Each N-K frame 102 is an M+2m₁×N+2n₁ area.The source block 100 is shown in the center of the area in FIG. 1. Theparts of the images that match can be detected by correlating each partof each image frame against other image frame using the distortionmeasurer. The compression scheme uses this detection to compress thedata, and hence send less information about the image.

This device can also be part of a general-purpose DSP. Such a device iscontemplated for use in video camcorders, teleconferencing, PC videocards, and HDTV. In addition, the general-purpose DSP is alsocontemplated for use in connection with other technologies utilizingdigital signal processing such as voice processing used in mobiletelephony, speech recognition, and other applications.

The speed of the overall distortion detection process can be increased.One way is by using hardware that allows each SAD device to carry outmore operations in a cycle. This, however, can require more expensivehardware.

Another way is to increase the effective pixel throughput by addingadditional SAD devices. This can also increase cost, however, since itrequires more SAD devices.

Faster search algorithms attempt to use the existing hardware moreeffectively.

The block SAD compares the source group against the “search group”. Thesource group and the search group move throughout the entire image sothat the SAD operation calculates the overlap between the two groups.Each block in the source group will be compared to multiple blocks ineach of the search regions.

A typical SAD unit operates on two, 16 by 16 elements to overlay thoseelements on one another. This overlay process calculates 16×16=256differences. These are then accumulated to represent the totaldistortion.

The SAD requires certain fundamental operations. A difference betweenthe source X_(ij) and the search Y_(ij) must be formed. An absolutevalue |X_(ij)−Y_(ij) |is formed. Finally, the values are accumulated,${SAD} = {\sum\limits_{i = 0}^{N - 1}\quad{\sum\limits_{j = 0}^{n - 1}{{{X_{ij} - Y_{ij}}}.}}}$

A basic accumulation structure is shown in FIG. 2 Arithmetic logic unit200 receives X_(ij) and Y_(ij) from data buses 198,199 connectedthereto, and calculates X_(ij)−Y_(ij). The output 201 is inverted byinverter 202. Both the inverted output, and the original, are sent tomultiplexer 204 which selects one of the values based on a sign bit 205.A second arithmetic logic unit 206 combines these to form the absolutevalue. The final values are stored in accumulation register 208.Effectively, this forms a system of subtract, absolute, accumulate, asshown in FIG. 2.

FIG. 2 shows a single SAD computation unit. As noted above, multiplecomputation units could be used to increases the throughput. If thenumber of computation units is increased, that increases, in theory, thepixel throughput per cycle.

The present inventor noted, however, that increase in pixel throughputis not necessarily linearly related to the number of units. In fact,each frame is somewhat correlated with its neighboring frames. Inaddition, different parts of any image are often correlated with otherparts of the image. The efficiency of the compression may be based oncharacteristics of the images. The present application allows using themultiple SAD devices in different modes, depending on the efficiency ofcompression.

The present application uses the architecture shown in FIGS. 3A and 3B.The same connection is used in both FIGS. 3A and 3B, but thecalculations are partitioned in different ways.

FIG. 3A shows each SAD device 300, 302 being configured as a whole SAD.Each SAD receives a different block, providing N block SAD calculations.Effectively, unit 301, therefore, calculates the relationship between a16 by 16 reference and a 16 by 16 source, pixel by pixel. Unit 2, 302calculates the result the difference 16 by 16 source and the 16 by 16search pixel by pixel. The alternative shown in FIG. 3B. In thisalternative, configuration each single SAD 300, 302 performs a fractionof a single block SAD calculation. Each of the N computation unitsprovides 1/N of the output. This “partial SAD” operation means that eachof the 8 bit subtract absolute accumulate units have calculated 1/N ofthe full SAD calculation configured to that unit.

The overall system that determines the whole or partial should be usedbased on previous results as described herein. This in turn can reducethe number of calculations that is carried out.

One way to determine whether whole or partial is used is to assume thattemporally close images have correlated properties. A first cycle can becalculated using the whole SAD mode, and a second cycle can becalculated using the partial SAD mode. The cycle which works faster istaken as the winner, and sets the SAD mode. This calculation can berepeated every X cycles, where X is the number of cycles after whichlocal temporal correlation can no longer be assumed. This can be done ina logic unit, which carries out the flowchart of FIG. 7, describedherein.

Throughput can also be increased by an “early exit” technique asdescribed herein.

The complete SAD calculation for 16×16 elements can be written as|p₁r−p₁s|+|p₂r−p₂s|+. . . |p₂₅₆s−p₂₅₆r|  (1)If all of these calculations were actually carried out, the calculationcould take 256/N cycles, where N is the number of SAD units. It isdesirable to stop the calculation as soon as possible. Interim resultsof the calculation are tested. These interim results are used todetermine if enough information has been determined to find a minimumdistortion. The act of testing, however, can consume cycles.

The present application describes a balance between this consumption ofcycles and the determination of the minimum distortion. FIG. 4illustrates the tradeoff for a 16×16 calculation using 4 SAD devices.Line 400 in FIG. 4 represents the cycle count when there is no earlyexit. The line is horizontal representing that the cycle count withoutearly exit is always 256/4=64.

The cycle counts for early exit strategies are shown in the sloped lines402, 404, 406 and 408. Line 404 represents one test every sixteenpixels, line 406 represents one test every thirty-two pixels (1/8) andline 408 represents one test every sixty-four pixels (1/16). Note thatwhen the lines 402-408 are above line 400, the attempt at early exit hasactually increased the overall distortion calculation time. Line 402represents the cycle consumption where zero overhead is obtained forexit testing. That is, when a test is made, the exit is alwayssuccessful. Line 402 is the desired goal. An adaptive early exit schemeis disclosed for doing so.

Block I is first processed using any normal strategy known in the art tofind a minimum distortion. This can be done using test patterns, whichcan be part of the actual image, to find the distortion. This minimumdistortion is used as the baseline; and it is assumed that block I+n,where n is small, has that same minimum distortion. Two basic parametersare used.

Kexit(N) represents the number of pixels that have been processedpreviously for a search region before an early exit is achieved.

Aexit(N) represents the state of the partial accumulator sign bits, atthe time of the last early exit for a search region.

For these blocks I+n, the SAD calculation is terminated when thedistortion exceeds that threshold. This forms a causal system usingprevious information that is known about the search region.

The usual system is based on the image characteristics within a searchregion being some probability of maintaining common characteristics fromtime to time. The time between frames is between {fraction (1/15)} and{fraction (1/30)} of second, often fast enough that minimal changesoccur during those times above some noise floor related to measurablesystem characteristics. Also, there are often regions of an image whichmaintains similar temporal characteristics over time.

According to the present application, the accumulator unit for each SADcan be loaded with the value (−least/n), where “least” represents theminimum distortion that is measured in the block motion search for theregion. Many SAD's are calculated for each search region. The first SADcalculating for the region is assigned the “Least” designation. FutureSADs are compared to this, to see if a new “Least” value has beenestablished. When the accumulators change sign, the minimum distortionhas been reached. Moreover, this is indicated using only the existingSAD structure, without an additional calculation, and hence additionalcycle(s) for the test.

A test of the character of the image can be used to determine how manyof the accumulators need to switch before establishing the early exit.For example, if source and target regions are totally homogeneous, thenall the accumulators should change sign more or less at the same time.When this happens, any one of the running SAD calculations exceeding theprevious least measurement can be used to indicate that an early exit isin order.

This, however, assumes total image homogeneity. Such an assumption doesnot always hold. In many situations, the multiple accumulators of thedifferent SAD units will not be increasing at the same rate. Moreover,the different rate of increase between the accumulators may be relateddirectly to the spatial frequency characteristics of the differencesthemselves, between the source and target block, and also to the methodof sampling the data. This can require more complex ways of consideringhow to determine early exit, based on what happens with the SAD units.

One operation is based on the probability associated with a split SADstate; where not all of the SAD units are in the same state. Thisdifference in rate of increase between the accumulators is related tothe spatial frequency characteristics of the difference between thesource and target block. Since these spatial frequency characteristicsare also correlated among temporally similar frames, the informationfrom one frame may also be applied to analysis of following frames.

This is explained herein with reference to variables—where A₁, A₂, A₃ .. . A_(n) are defined as events associated with a split SAD calculation.

The events can be defined as follows:Event A_(i)=SAD_(i)≧0 where SAD <0 for i≠j.

This conceptually means that the event A_(i) is defined as occuring whenSAD unit i is positive and all the remaining SAD units are negative.This would occur, for example, when the accumulators were increasing atdifferent rates. This can also be defined as combined events,specifically:Event B_(i,j)=A_(i)∪A_(j)=SAD_(i)≧0 for SAD_(j)≧0, and

where SAD_(K)<0 for k≠i, j. This means that event B_(i,j) is defined as“true” when A_(i) exists and A_(j) are true, but all other A_(k) arefalse. The concept of defining the operations in terms of events can beextended to include all the possible combinations of i, j and k. Thisyields, for 4 SAD units, a total of 16 combinations. For larger numbersof SAD units, it leads to other numbers of combinations, and possiblyusing more variables, such as i, j, k and m or others.

Describing this scenario in words, each event “B” is defined as the sumof the specified accumulators being greater than 0. Each of thesecombinations is defined as a probability. For 4 SAD units, there aretotal of 16 possible states of accumulators. These can be groupedaccording to how they are handled.

A first trivial possibility isP(B|Ā ₁ ∩Ā ₂ ∩Ā ₃ ∩Ā ₄)=0.

This means that the probability that sum of the accumulators is >0,given that none of the accumulators has exceeded 0, is 0.

The opposite is also true:P(B|A ₁ ∩A ₂ ∩A ₃ ∩A ₄)=1;

Which means that the probability of the sum of all the accumulators isset, given that none of them are set, is also 1.

Excluding these trivial characteristics, there are 14 nontrivialcombinations. The first group includes four cases where one of theaccumulators is set and the remaining three are not set:

P(B|A₁∪(Ā₂∩Ā₃∩Ā₄),

P(B|A₂∪(Ā₁∩Ā₃∩Ā₄),

P(B|A₃∪(Ā₁∩Ā₂∩Ā₄),

P(B|A₄∪(Ā₁∩Ā₂∩Ā₃).

Another group represents those conditions where two of the accumulatorsare set, and the other two accumulators are not set. These combinationsare written as:

P(B|A₁∩A₂)∪(Ā₃∩Ā₄)

P(B|A₁∩A₃)∪(Ā₂∩Ā₄)

P(B|A₁∩A₄)∪(Ā₂∩Ā₃)

P(B|A₂∩A₃)∪(Ā₁∩Ā₄)

P(B|A₂∩A₄)∪(Ā₁∩Ā₃)

P(B|A₃∩A₄)∪(Ā₁∩Ā₂)

Finally, the following group represents the cases where threeaccumulators are set and one accumulator is not set

P(B|A₁∩A₂∩A₃)∪Ā₄)

P(B|A₂∩A₃∩A₄)∪Ā₁)

P(B|A₁∩A₃∩A₄)∪Ā₂)

P(B|A₁∩A₂∩A₄)∪Ā₃).

The present embodiment recognizes that each of these groups, and in facteach of these situations, represents a different condition in the image.Each group or each situation can be handled differently.

This system operates as above, and as described with reference to theflowchart of FIG. 5. The final goal is to complete the calculation, andhence to exit, sooner. This is shown in FIG. 5 by first, determiningmatching characteristics of two images; a source image and a searchimage at 550. The matching characteristics are calculated without anyearly exit. The minimum distortion is found at 555 and the conditionswhen that minimum distortion existed are found at 560.

The conditions at 560 can include a grouping type that existed at thetime of minimum distortion, or the specific condition among the 14possibilities.

At 570 a subsequent image part is tested. This subsequent part can beany part that is correlated to the test part. Since temporallycorrelated images are assumed to be correlated, this can extend to anytemporally correlated part.

The image source and search are tested, and a determination of thespecific groupings that occurred at the time of minimum distortion isfound at 575. An early exit is then established, at 580.

The early exit, once determined, can be carried out in a number ofdifferent ways.

FIG. 6 a shows a system of carrying out the early exit using an earlyexit or “EE” flag. N SAD units are shown, where in this embodiment, Ncan be 4. Each SAD unit includes the structure discussed above, andspecifically ALUs, inverters, and accumulators.

The output of each of the accumulators is coupled to a combinatoriallogic unit 600 which arranges the outputs. This can be used to carry outthe group determination noted above. The combinatorial logic unit iscarried out using discrete logic gates, e.g., defined in hardwaredefinition language. The gates are programmed with an option based onthe selected group. Different images and parts may be processedaccording to different options.

For each option, the combination of states, e.g., the group discussedabove, is coded. The combinatorial logic monitors the accumulators ofall the SAD units. Each state is output to a multiplexer.

When those accumulators achieve a state that falls within the selectedcoding, an early exit flag is produced. The early exit flag means thatthe hardware has determined an appropriate “fit”. This causes theoperation to exit.

FIG. 6B shows an alternative system, in which the states of theaccumulators are sensed by a hardware status register 600. The statusregister is set to a specified state by the condition of theaccumulators. The status register stores the specified condition thatrepresents the early exit. When that specified condition is reached, theearly exit is established.

The way in which the adaptive early exit is used, overall, is describedin reference to FIG. 7. At 700, the video frame starts. 705 representsbuffering both frame M and frame M+1. 710 is a determination if theblock history model needs update. This can be determined by, forexample, monitoring of the time since a previous frame update. Forexample, x seconds can be established as a time before a new update isnecessary.

If the model needs updating, then the process continues by loading theaccumulators with 0xFF01 and setting the local variable N=1 at 715. At720, the system obtains SAD search region N and uses the periodic exittest T_(exit)=1/16 . . . , at step 725 the exit test is performed. Ifsuccessful, a local variable Kexit(N), which is the pixels before exitand Aexit(N) which is an summary of accumulators 1 through 4 before exitrestored. The local variable n is also incremented at step 730. Thisestablishes the local parameters, and the process continues.

In a subsequent cycle the block history of update does not need to beredone at step 710, and hence control passes to step 735. At this step,the previously stored Kexit and AEexit are read. This is used as the newcount at step 740 to set target block flags.

At step 745, a search for block N is established, an a exit and Kexitare updated at step 750. N is incremented. At step 755, a determinationis made whether N is equal to 397. 397 is taken as the number of framesin the buffer, since there are 396, 16×16 blocks in a 352×288 image.However, this would be adjusted for different size sizes as applicable.

Again, the temporal variations of large portions of an image are likelyto remain unchanged. Therefore, when the partial accumulators have aspecific sign bit, their state produces significant advantages.Moreover, the time between frames is usually on the order of {fraction(1/15)} to {fraction (1/30)} of a second. Finally, regions within theimage maintain their localized characteristics, and therefore theirspatial frequency may be correlated.

Although only a few embodiments have been disclosed, other modificationsare possible.

1. An apparatus, comprising: a plurality of image processing elements; acircuit that stores first states of said image processing elements at atime of a specified image processing result; and an early exit circuitthat determines a completion of a calculation based on comparing currentstates with said first states, wherein the current states of said imageprocessing elements are characterized by one or more characteristics ofsaid image processing elements at a current time; wherein saidcharacteristics include arithmetic states of said image processingelements; wherein said image processing elements include accumulatorstherein, and said characteristics include sign bits of saidaccumulators.
 2. An apparatus as in claim 1, further comprising a videoobtaining element.
 3. An apparatus as in claim 2, wherein said videoobtaining element is a video camera.
 4. An apparatus as in claim 1,wherein said characteristics comprise states of groups of said imageprocessing elements.